Backend Engineer

Channel3 is building a database of every product on the internet. People have wanted to do this for decades, but it wasn’t possible until now – AI is finally smart enough to structure the world’s messy product data, and inexpensive enough to do so at scale. ### Our vision: from in-store, to online, to AI-native We believe agentic commerce is as important as in-store and online channels; that’s where “Channel3” comes from. We plan to become the central node of agentic commerce, powering every AI transaction and taking a cut of GMV. We see Channel3 becoming as foundational to AI commerce as Stripe is to payments or Plaid is to fintech. ### The problem Agentic commerce is bottlenecked by messy product data. (See [“OpenAI’s Shopping Ambitions Hit Messy Data Reality”](https://www.theinformation.com/articles/openais-shopping-ambitions-hit-messy-data-reality)) Product data is inconsistent at best, completely wrong at worst — that is, if it exists at all. ### The opportunity [McKinsey estimates](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-agentic-commerce-opportunity-how-ai-agents-are-ushering-in-a-new-era-for-consumers-and-merchants) that “by 2030, the US B2C retail market alone could see up to $1 trillion in orchestrated revenue from agentic commerce, with global projections reaching as high as $3 trillion to $5 trillion.” That future is impossible without great product data. ### The founders [Alex](https://www.linkedin.com/in/alexanderschiff/) (CEO) and [George](https://www.linkedin.com/in/george-lawrence-aab0011a6/) (CTO) have been friends since the first day of Duke. Alex began coding at 9, started his first company at 12, and most recently led AI projects at [studio.com](http://studio.com), where he got this idea. George published research on automating astronomy with robots at 18, and worked on big-data problems at Palantir for the past 2 years. Unfortunately, we are not able to sponsor visas at this time. You must have authorization to work in the US. ### Our progress so far * We’ve indexed 100M+ products * 2,000+ developers have started using our API * We’re scoping pilots with some serious enterprise customers * We’ve partnered with 5,000+ brands, including Walmart, Best Buy, and Home Depot ### What you’ll do * **Write code to understand 1B products**. We stitch together the latest language, embedding, image, and segmentation models to build a true understanding of each and every product. * Product pages vary greatly across retailers. We’ve built a computer-vision system that can understand any PDP, with no custom code or manual intervention on any site. * Deduplicating products across merchants is really hard. Products are described differently, often have different images, and almost never have stable identifiers. We use multimodal models to help us understand different products and match them. * Understanding what variants a product comes in might be even harder. We want to know what colors and sizes every shirt comes in, and what configurations customers can order for a new couch. * **Build world-class search**. Developers should be able to find “outdoor grills from Weber, less than $1000, with 4 burners,” “running sneakers under 300g, size 12,” or “this couch \[image\] but in green” — in under a second. * Embeddings don’t get us there. We’re always looking to add structured data that lets developers construct powerful queries with deterministic filters. * Lately, we’ve been ideating about how to return “good” results, which we’ve learned is not just the highest keyword/semantic match. * **Engineer for reliability and cost**. Create evals to measure drift, and guardrails to prevent regressions and hallucinations. Be clever and scrappy to reduce database cost and token usage everywhere we can. * We use billions and billions of tokens a month, mostly on open-source models. We embed every product, we look at every image with AI, and we run dozens of calls per product — it’s necessary, but it adds up. We’re always looking to reduce token usage, rely on a smaller model, or do some clever context engineering to reduce input tokens. * We structure the world’s product data — and we have to store it and search through it (both internally to find matches and to power our API). We’ve taken many steps towards making this economical, and will continue to do so. ### Why join us * **The time is right:** AI is finally smart and inexpensive enough to make a universal product graph technically possible; agentic commerce creates immediate demand and a clear monetization path. * **We’re moving fast:** We’re up and running, with 2,000+ developers building on our API, customers eagerly awaiting features, and 25M+ products processed every day. * **You’ll have direct ownership** of technical and infrastructure decisions, roadmap, and prioritization. You’ll help us grow the team and build our culture from the ground up. * **We’re having lots of fun:** We enjoy working together, we take pride in building really good tech, and we all look forward to coming to work each day. ### How we work * We work out of our sunny office in Flatiron (54 W 21st St) in-person together Monday through Friday. * We provide dinner Monday-Thursday, plus any snacks you want (we’re across the street from a Trader Joe’s). ### Investors We raised a $6M seed round in August 2025, led by [Matrix](https://matrix.vc/) (Apple, FedEx, Afterpay, Oculus) with participation from [Ludlow](https://www.ludlowventures.com/) (Honey, StockX), Y Combinator, Paul Graham, a16z + Index scouts, and a dozen angels (mostly ex-YC founders). Visa sponsorship is not available for this role.